FN Archimer Export Format
PT J
TI Modeling fleet response in regulated fisheries: An agent-based approach
BT 
AF SOULIE, J
   THEBAUD, Olivier
AS 1:1;2:2;
FF 1:;2:PDG-DOP-DCB-EM;
C1 Lab Informat Littoral, F-62228 Calais, France.
   IFREMER, Marine Econ Serv, F-29280 Plouzane, France.
C2 UNIV LITTORAL COTE D'OPALE, FRANCE
   IFREMER, FRANCE
SI BREST
SE PDG-DOP-DCB-EM
IN WOS Ifremer jusqu'en 2018
   copubli-france
   copubli-univ-france
IF 0.432
TC 34
UR https://archimer.ifremer.fr/doc/2006/publication-1893.pdf
LA English
DT Article
DE ;Short run fisheries dynamics;Multi agent simulation;Bio economic modeling
AB Understanding the dynamics of fishing effort plays a key role in predicting the impacts of regulatory measures on fisheries. In recent years, there has been a growing interest in the use of bio-economic models to represent and analyze the short-term dynamics of fishing effort in response to regulation in the fisheries management literature. In this literature, fishing firms are usually modeled as autonomous decision-making units determining their harvest strategies so as to maximize profit, given technical and institutional constraints. The overall dynamics of a fishery is modeled as the result of these individual choices, and of interactions between individual choices due to the impacts of harvesting on the fish stock and/or problems of congestion. Applications have, for example, been related to the discussion of closed areas as fisheries management tools. A multi-agent model of a fishery targeting different species in different areas was developed to analyze the implications of taking into account the response of fishing fleets to such regulatory controls. The model is based on the Cormas platform developed for the simulation of the dynamics of common resource systems. An advantage of the multi-agent approach is that it allows a greater degree of complexity than standard bioeconomic modeling tools, by focusing on local, rather than global, interactions. Simulation results are presented to illustrate how the model can be used to analyze the consequences of regulatory measures such as temporary fishing bans on the allocation of fishing effort between target species and areas, and the ensuing economic impacts of these measures. (c) 2005 Elsevier Ltd. All rights reserved.
PY 2006
PD SEP
SO Mathematical and Computer Modelling
SN 0895-7177
PU Elsevier
VL 44
IS 5-6
UT 000239731700011
BP 553
EP 564
DI 10.1016/j.mcm.2005.02.011
ID 1893
ER

EF